Validation Of Novel Biomarkers Research Articles

Oxidative stress in the brain-lung crosstalk: cellular and molecular perspectives.

Oxidative stress is caused by an imbalance between the production of reactive oxygen species (ROS) and the body's ability to counteract their harmful effects, playing a key role in the pathogenesis of brain and lung-related diseases. This review comprehensively examines the intricate mechanisms by which oxidative stress influences cellular and molecular pathways, contributing to neurodegenerative, cardiovascular, and respiratory disorders. Emphasizing the detrimental effects on both brain and lung health, we discuss innovative diagnostic biomarkers, such as 8-hydroxy-2'-deoxyguanosine (8-OHdG), and the potential of antioxidant therapies. For these topics, we provide insights into future research directions in the field of oxidative stress treatment, including the development of personalized treatment approaches, the discovery and validation of novel biomarkers, and the development of new drug delivery systems. This review not only provides a new perspective on understanding the role of oxidative stress in brain and lung-related diseases but also offers new insights for future clinical treatments.

A cloud-based precision oncology framework for whole genome sequence analysis

Cancer is one of the wide-ranging diseases which have a high mortality rate impacting globally. This scenario can be switched by early detection and correct precision treatment, a major concern for cancer patients. Clinicians can figure out the best-suited treatments for cancer patients by analyzing the patient's genome, which will treat the patient well and minimize the chances of side effects as well. Therefore, we have developed a fast, robust, and efficient solution as our precision oncology framework based on the whole genome sequencing of the individual's DNA. This platform can perform the entire genomic analysis, starting from the quality assessment of the input file to the variant annotation and functional prediction, followed by a certain level of interpretation. This analysis helps in the molecular profiling of the tumors for the identification of the targetable alterations. It takes in FASTQ or BAM file as an input and provides us with two output reports: a primary report, which consists of the patients' details, a summary of the analysis, and a secondary report, which is an elaborated report comprised of numerous results obtained from the analysis such as base changes, codon changes, amino acid changes, TMB analysis, MSI analysis, the variant frequency with its effects and impacts, affected biomarkers, etc. This framework can be effectively utilized for cancer treatment guidance, identification and validation of novel biomarkers, oncology research & development, genomic analysis, and gene manipulation.

Combining causal and correlative approaches to discover biomarkers of response to paclitaxel.

We recently discovered a putative paclitaxel response predictive biomarker for glioblastoma and breast cancer using the whole genome CRISPR knockout screen. The biomarker candidate was validated in two independent breast cancer patient cohorts that received taxane treatment. To further evaluate the potential application of this biomarker in the clinic for patients with glioblastoma, a prospective validation in cohorts of patients with glioblastoma is essential and will be performed as part of our ongoing phase II clinical trial (NCT04528680). The validation of novel biomarkers of susceptibility to therapy is critical to elucidate the efficacy signal of therapeutic agents. This is especially important in the context of glioblastoma, where therapeutic benefit is variable and unpredictable, leading to negative trials, yet the outcome of subset of patients has outperformed expectations.

Identification and Validation of Novel Biomarkers Related to the Calcium Metabolism Pathway in Hypertension Patients Based on Comprehensive Bioinformatics Methods

Identification and Validation of Novel Biomarkers Related to the Calcium Metabolism Pathway in Hypertension Patients Based on Comprehensive Bioinformatics Methods

Identification and validation of novel biomarkers for cold-dampness syndrome of rheumatoid arthritis based on integration of multiple bioinformatics methods

This study aims to identify the novel biomarkers of cold-dampness syndrome(RA-Cold) of rheumatoid arthritis(RA) by gene set enrichment analysis(GSEA), weighted gene correlation network analysis(WGCNA), and clinical validation. Firstly, transcriptome sequencing was carried out for the whole blood samples from RA-Cold patients, RA patients with other traditional Chinese medicine(TCM) syndromes, and healthy volunteers. The differentially expressed gene(DEG) sets of RA-Cold were screened by comparison with the RA patients with other TCM syndromes and healthy volunteers. Then, GSEA and WGCNA were carried out to screen the key DEGs as candidate biomarkers for RA-Cold. Experimentally, the expression levels of the candidate biomarkers were determined by RT-qPCR for an independent clinical cohort(not less than 10 cases/group), and the clinical efficacy of the candidates was assessed using the receiver operating characteristic(ROC) curve. The results showed that 3 601 DEGs associated with RA-Cold were obtained, including 106 up-regulated genes and 3 495 down-regulated genes. The DEGs of RA-Cold were mainly enriched in the pathways associated with inflammation-immunity regulation, hormone regulation, substance and energy metabolism, cell function regulation, and synovial pannus formation. GSEA and WGCNA showed that recombinant proteasome 26S subunit, ATPase 2(PSMC2), which ranked in the top 50% in terms of coefficient of variation, representativeness of pathway, and biological modules, was a candidate biomarker of RA-Cold. Furthermore, the validation results based on the clinical independent sample set showed that the F1 value, specificity, accuracy, and precision of PSMC2 for RA-Cold were 70.3%, 61.9%, 64.5%, and 81.3%, respectively, and the area under the curve(AUC) value was 0.96. In summary, this study employed the "GSEA-WGCNA-validation" integrated strategy to identify novel biomarkers of RA-Cold, which helped to improve the TCM clinical diagnosis and treatment of core syndromes in RA and provided an experimental basis for TCM syndrome differentiation.

Post-mortem detection of neuronal and astroglial biochemical markers in serum and urine for diagnostics of traumatic brain injury.

Currently available epidemiological data shows that traumatic brain injury (TBI) represents one of the leading causes of death that is associated with medico-legal practice, including forensic autopsy, criminological investigation, and neuropathological examination. Attention focused on TBI research is needed to advance its diagnostics in ante- and post-mortem cases with regard to identification and validation of novel biomarkers. Recently, several markers of neuronal, astroglial, and axonal injury have been explored in various biofluids to assess the clinical origin, progression, severity, and prognosis of TBI. Despite clinical usefulness, understanding their diagnostic accuracy could also potentially help translate them either into forensic or medico-legal practice, or both. The aim of this study was to evaluate post-mortem pro-BDNF, NSE, UCHL1, GFAP, S100B, SPTAN1, NFL, MAPT, and MBP levels in serum and urine in TBI cases. The study was performed using cases (n = 40) of fatal head injury and control cases (n = 20) of sudden death. Serum and urine were collected within ∼ 24h after death and compared using ELISA test. In our study, we observed the elevated concentration levels of GFAP and MAPT in both serum and urine, elevated concentration levels of S100B and SPTAN1 in serum, and decreased concentration levels of pro-BDNF in serum compared to the control group. The obtained results anticipate the possible implementation of performed assays as an interesting tool for forensic and medico-legal investigations regarding TBI diagnosis where the head injury was not supposed to be the direct cause of death.

Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases.

A biomarker is any measurable biological moiety that can be assessed and measured as a potential index of either normal or abnormal pathophysiology or pharmacological responses to some treatment regimen. Every tissue in the body has a distinct biomolecular make-up, which is known as its biomarkers, which possess particular features, viz., the levels or activities (the ability of a gene or protein to carry out a particular body function) of a gene, protein, or other biomolecules. A biomarker refers to some feature that can be objectively quantified by various biochemical samples and evaluates the exposure of an organism to normal or pathological procedures or their response to some drug interventions. An in-depth and comprehensive realization of the significance of these biomarkers becomes quite important for the efficient diagnosis of diseases and for providing the appropriate directions in case of multiple drug choices being presently available, which can benefit any patient. Presently, advancements in omics technologies have opened up new possibilities to obtain novel biomarkers of different types, employing genomic strategies, epigenetics, metabolomics, transcriptomics, lipid-based analysis, protein studies, etc. Particular biomarkers for specific diseases, their prognostic capabilities, and responses to therapeutic paradigms have been applied for screening of various normal healthy, as well as diseased, tissue or serum samples, and act as appreciable tools in pharmacology and therapeutics, etc. In this review, we have summarized various biomarker types, their classification, and monitoring and detection methods and strategies. Various analytical techniques and approaches of biomarkers have also been described along with various clinically applicable biomarker sensing techniques which have been developed in the recent past. A section has also been dedicated to the latest trends in the formulation and designing of nanotechnology-based biomarker sensing and detection developments in this field.

Mass Spectrometry Contribution to Pediatric Cancers Research

For over four decades, mass spectrometry-based methods have provided a wealth of information relevant to various challenges in the field of cancers research. These challenges included identification and validation of novel biomarkers for various diseases, in particular for various forms of cancer. These biomarkers serve various objectives including monitoring patient response to the various forms of therapy, differentiating subgroups of the same type of cancer, and providing proteomic data to complement datasets generated by genomic, epigenetic, and transcriptomic methods. The same proteomic data can be used to provide prognostic information and could guide scientists and medics to new and innovative targeted therapies The past decade has seen a rapid emergence of epigenetics as a major contributor to carcinogenesis. This development has given a fresh momentum to MS-based proteomics, which demonstrated to be an unrivalled tool for the analyses of protein post-translational modifications associated with chromatin modifications. In particular, high-resolution mass spectrometry has been recently used for systematic quantification of chromatin modifications. Data generated by this approach are central in the search for new therapies for various forms of cancer and will help in attempts to decipher antitumor drug resistance. To appreciate the contribution of mass spectrometry-based proteomics to biomarkers discovery and to our understanding of mechanisms behind the initiation and progression of various forms of cancer, a number of recent investigations are discussed. These investigations also include results provided by two-dimensional gel electrophoresis combined with mass spectrometry.

HPV-Based Self-Sampling in Cervical Cancer Screening: An Updated Review of the Current Evidence in the Literature.

Identifying and reaching women at higher risk for cervical cancer is all-important for achieving the ambitious endpoints set in 2020 by the WHO for global cervical cancer control by 2030. HPV-based (vaginal) self-sampling (SS) represents a cost-effective screening strategy, which has been successfully implemented during the last decade both in affluent and constrained settings. Among other advantages, SS strategies offer convenience, diminished costs, flexibility to obtain a sample in the office or home, avoiding a pelvic exam and uncomfortable appointment with a healthcare professional, as well as social and cultural acceptability. SS implementation has been globally boosted during the COVID-19 pandemic. In pragmatic terms, social distancing, local lockdowns, discontinuation of clinics and reallocation of human and financial resources challenged established clinician-based screening; self-collection strategies apparently surpassed most obstacles, representing a viable and flexible alternative. With time, sufficient reassuring data has accumulated regarding specially designed SS devices, aspects of sample preparation, transport and storage and, importantly, optimization of validated PCR-based HPV testing platforms for self-collected specimens. Suboptimal rates of clinical follow-up post-SS screening, as well as overtreatment with reliance solely on molecular assays, have both been documented and remain concerning. Therefore, effective strategies are still required to ensure linkage to follow-up testing and management following positive SS results by trained health professionals with knowledge of HPV biology and management algorithms. Because of the prolonged SS screening intervals, implementation data are limited regarding subsequent screening rounds of SS-screened individuals; however, these are accumulating gradually. With further refinement of assays and validation of novel biomarkers in self-collected samples, there is a clear potential for increasing SS accuracy and PPV. The potential differentiation of self-collection protocols for vaccinated versus non-vaccinated individuals also represents an open issue. In conclusion, HPV-based self-collection techniques can effectively address limited uptake alongside other conventional cervical screening drawbacks; however, assays, logistics and infrastructures need further optimization to increase the efficacy, effectiveness and cost-effectiveness of SS approaches.

Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning.

Objectives: Osteosarcoma is the most common primary malignant tumor in children and adolescents, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past decades. Effective biomarkers in diagnosing osteosarcoma are warranted to be developed. This study aims to explore novel biomarkers correlated with immune cell infiltration in the development and diagnosis of osteosarcoma. Methods: Three datasets (GSE19276, GSE36001, GSE126209) comprising osteosarcoma samples were extracted from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression. Then, differentially expressed genes (DEGs) were identified by limma and potential biological functions and downstream pathways enrichment analysis of DEGs was performed. The machine learning algorithms LASSO regression model and SVM-RFE (support vector machine-recursive feature elimination) analysis were employed to identify candidate hub genes for diagnosing patients with osteosarcoma. Receiver operating characteristic (ROC) curves were developed to evaluate the discriminatory abilities of these candidates in both training and test sets. Furthermore, the characteristics of immune cell infiltration in osteosarcoma, and the correlations between these potential genes and immune cell abundance were illustrated using CIBERSORT. qRT-PCR and western blots were conducted to validate the expression of diagnostic candidates. Results: GEO datasets were divided into the training (merged GSE19276, GSE36001) and test (GSE126209) groups. A total of 71 DEGs were screened out in the training set, including 10 upregulated genes and 61 downregulated genes. These DEGs were primarily enriched in immune-related biological functions and signaling pathways. After machine learning by SVM-RFE and LASSO regression model, four biomarkers were chosen for the diagnostic nomogram for osteosarcoma, including ASNS, CD70, SRGN, and TRIB3. These diagnostic biomarkers all possessed high diagnostic values (AUC ranging from 0.900 to 0.955). Furthermore, these genes were significantly correlated with the infiltration of several immune cells, such as monocytes, macrophages M0, and neutrophils. Conclusion: Four immune-related candidate hub genes (ASNS, CD70, SRGN, TRIB3) with high diagnostic value were confirmed for osteosarcoma patients. These diagnostic genes were significantly connected with the immune cell abundance, suggesting their critical roles in the osteosarcoma tumor immune microenvironment. Our study provides highlights on novel diagnostic candidate genes with high accuracy for diagnosing osteosarcoma patients.

AIT 2023: Current innovation and future outlook.

Although used for over 100 years, allergen immunotherapy (AIT) is still an indispensable tool in modern allergy managemen20t due to its potential to cure allergic diseases. Its current rapid development through the application of personalized and precision medicine approaches is strongly supported by advances in mHealth, component-resolved diagnosis (CRD)-based diagnostics, validation of novel biomarkers, advanced data management, and development of novel preparations. This review summarizes the key advances in the field and shows the perspectives for further development of next-generation AIT treatments.

TRIAD multi‐dimensional biobank for biomarker discovery

AbstractBackgroundThe Translational Biomarkers in Aging and Dementia (TRIAD) is a longitudinal, biomarker‐based cohort designed to study interactions between the pathophysiological processes driving to dementia. Currently, 50 million people worldwide have dementia [1]. PET and MRI imaging have been widely used to assess disease stage, though expensive and not readily accessible [2]. More recently though, breakthroughs in blood‐based biomarkers have proven a useful, available, and more cost‐effective way of evaluating amyloid and tau positivity. The TRIAD Biobank provides a unique framework to study disease stage using blood‐based biomarkers in conjunction with PET and CSF studies. Our cohort allows for validation of novel biomarkers as we simultaneously acquire gold standard second generation imaging acquisitions with full spectrum fluid biomarkers (CSF, saliva, plasma, urine). Importantly, the data acquired is integrated in a database compliant to multi‐dimensional biomarker analysis.MethodFigure 1 depicts the cohort's banked fluids by diagnostic group. Figure 2 depicts the cohort's banked fluids by sex grouping.ResultThe TRIAD Biobank stores 1,034 blood samples from 655 participants, 658 of these being follow‐up collections. Additionally, cerebrospinal fluid has been collected from 381 individuals accounting for 520 collections in the bank, 812 urine collections from 662 individuals and 591 saliva samples from 432 individuals.Additionally, the cohort has collected 761 tau PET scans using [18F]MK6240, 644 amyloid PET scans using [18F]AZD4694 and 804 MRI using 3 Tesla.ConclusionWith dementia being one of the leading, most costly causes of death, the need for early detection, more affordable, less invasive, and more readily available ways to determine disease stage is apparent [2]. The TRIAD multi‐dimensional biobank provides a wealth of resources to discover affordable biomarkers needed for early diagnosis and Alzheimer’s disease prevention.[1] Patterson, C. 2018. World Alzheimer Report 2018: The state of the art of dementia research, new frontiers. London, England: Alzheimer's Disease International.[2] Gauthier S, Rosa‐Neto P, Morais JA, & Webster C. 2021. World Alzheimer Report 2021: Journey through the diagnosis of dementia. London, England: Alzheimer’s Disease International.

TRIAD multi‐dimensional biobank for biomarker discovery

AbstractBackgroundThe Translational Biomarkers in Aging and Dementia (TRIAD) is a longitudinal, biomarker‐based cohort designed to study interactions between the pathophysiological processes driving to dementia. Currently, 50 million people worldwide have dementia [1]. PET and MRI imaging have been widely used to assess disease stage, though expensive and not readily accessible [2]. More recently though, breakthroughs in blood‐based biomarkers have proven a useful, available, and more cost‐effective way of evaluating amyloid and tau positivity. The TRIAD Biobank provides a unique framework to study disease stage using blood‐based biomarkers in conjunction with PET and CSF studies. Our cohort allows for validation of novel biomarkers as we simultaneously acquire gold standard second generation imaging acquisitions with full spectrum fluid biomarkers (CSF, saliva, plasma, urine). Importantly, the data acquired is integrated in a database compliant to multi‐dimensional biomarker analysis.MethodFigure 1 depicts the cohort's banked fluids by diagnostic group. Figure 2 depicts the cohort's banked fluids by sex grouping.ResultThe TRIAD Biobank stores 1,034 blood samples from 655 participants, 658 of these being follow‐up collections. Additionally, cerebrospinal fluid has been collected from 381 individuals accounting for 520 collections in the bank, 812 urine collections from 662 individuals and 591 saliva samples from 432 individuals.Additionally, the cohort has collected 761 tau PET scans using [18F]MK6240, 644 amyloid PET scans using [18F]AZD4694 and 804 MRI using 3 Tesla.ConclusionWith dementia being one of the leading, most costly causes of death, the need for early detection, more affordable, less invasive, and more readily available ways to determine disease stage is apparent [2]. The TRIAD multi‐dimensional biobank provides a wealth of resources to discover affordable biomarkers needed for early diagnosis and Alzheimer’s disease prevention.[1] Patterson, C. 2018. World Alzheimer Report 2018: The state of the art of dementia research, new frontiers. London, England: Alzheimer's Disease International[2] Gauthier S, Rosa‐Neto P, Morais JA, & Webster C. 2021. World Alzheimer Report 2021: Journey through the diagnosis of dementia. London, England: Alzheimer’s Disease International.

Identification and validation of novel biomarkers affecting bladder cancer immunotherapy via machine learning and its association with M2 macrophages.

Immunotherapy has shown promising results in bladder cancer therapy options. Analysis of open-access data was conducted using the R software. Open-access data were obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and IMvigor210 databases. Immunofluorescence and co-culture systems were utilized to validate the effect of PTHLH on M2 macrophage polarization. Here, through the combined (TCGA, GSE128959, GSE13507, and GSE83586) and IMvigor210 cohorts, we comprehensively investigated the biological and immune microenvironment differences in patients with diverse immunotherapy responses. Meanwhile, we found that M2 macrophage could affect bladder cancer immunotherapy sensibility. Moreover, based on the machine learning algorithm (LASSO logistics regression), PTHLH, BHMT2, and NGFR were identified, which all have good prediction abilities for patient immunotherapy. Then, a logistics regression model was established based on PTHLH, BHMT2, and NGFR, and each patient was assigned a logistics score. Subsequently, we investigated the difference in patients with high low logistics scores, including biological enrichment, immune microenvironment, and genomic characteristics. Meanwhile, data from the Human Protein Atlas database indicated a higher protein level of PTHLH in bladder cancer tissue. Immunofluorescence indicated that the knockdown of PTHLH in bladder cancer cells can significantly inhibit the M2 polarization of co-culture M0 macrophages. Our study investigated the difference between bladder cancer immunotherapy responders and non-responders. Meanwhile, the PTHLH was identified as a novel biomarker for bladder cancer immunotherapy.

Identification and Validation of Novel Biomarkers and Potential Targeted Drugs in Cholangiocarcinoma: Bioinformatics, Virtual Screening, and Biological Evaluation.

Cholangiocarcinoma (CCA) is a complex and refractor type of cancer with global prevalence. Several barriers remain in CCA diagnosis, treatment, and prognosis. Therefore, exploring more biomarkers and therapeutic drugs for CCA management is necessary. CCA gene expression data was downloaded from the TCGA and GEO databases. KEGG enrichment, GO analysis, and protein-protein interaction network were used for hub gene identification. miRNA were predicted using Targetscan and validated according to several GEO databases. The relative RNA and miRNA expression levels and prognostic information were obtained from the GEPIA. The candidate drug was screened using pharmacophore-based virtual screening and validated by molecular modeling and through several in vitro studies. 301 differentially expressed genes (DEGs) were screened out. Complement and coagulation cascades-related genes (including AHSG, F2, TTR, and KNG1), and cell cycle-related genes (including CDK1, CCNB1, and KIAA0101) were considered as the hub genes in CCA progression. AHSG, F2, TTR, and KNG1 were found to be significantly decreased and the eight predicted miRNA targeting AHSG, F2, and TTR were increased in CCA patients. CDK1, CCNB1, and KIAA0101 were found to be significantly abundant in CCA patients. In addition, Molport-003-703-800, which is a compound that is derived from pharmacophores-based virtual screening, could directly bind to CDK1 and exhibited anti-tumor activity in cholangiocarcinoma cells. AHSG, F2, TTR, and KNG1 could be novel biomarkers for CCA. Molport-003-703-800 targets CDK1 and work as potential cell cycle inhibitors, thereby having potential for consideration for new chemotherapeutics for CCA.

Relative quantification of sulfenic acids in plasma proteins using differential labelling and mass spectrometry coupled with 473 nm photo-dissociation analysis: A multiplexed approach applied to an Alzheimer's disease cohort

Cysteine (Cys) is subject to a variety of reversible post-translational modifications such as formation of sulfenic acid (Cys-SOH). If this modification is often involved in normal biological activities, it can also be the result of oxidative damage. Indeed, oxidative stress yields abnormal cysteine oxidations that affect protein function and structure and can lead to neurodegenerative diseases. In a context of population ageing, validation of novel biomarkers for detection of neurodegenerative diseases is important. However, Cys-SOH proteins investigation in large human cohorts is challenging due to their low abundance and lability under endogenous conditions. To improve the detection specificity towards the oxidized protein subpopulation, we developed a method that makes use of a mass spectrometer coupled with visible laser induced dissociation (LID) to add a stringent optical specificity to the mass selectivity. Since peptides do not naturally absorb in the visible range, this approach relies on the proper chemical derivatization of Cys-SOH with a chromophore functionalized with a cyclohexanedione. To compensate for the significant variability in total protein expression within the samples and any experimental bias, a normalizing strategy using free thiol (Cys-SH) cysteine peptides derivatized with a maleimide chromophore as internal references was used. Thanks to the differential tagging, oxidative ratios were then obtained for 69 Cys-containing peptides from 19 proteins tracked by parallel reaction monitoring (PRM) LID, in a cohort of 49 human plasma samples from Alzheimer disease (AD) patients. A statistical analysis indicated that, for the proteins monitored, the Cys oxidative ratio does not correlate with the diagnosis of AD. Nevertheless, the PRM-LID method allows the unbiased, sensitive and robust relative quantification of Cys oxidation within cohorts of samples.

Epitheliod hemagioendothelioma (EHE) patient-derived model (PDX): Drug activity assessment and validation of novel biomarkers.

11566 Background: EHE is an ultra-rare sarcoma marked by two specific fusions, WWTR1-CAMTA1 (̃90%) and YAP1-TFE3 (̃10%). The clinical course of EHE ranges from indolent to highly aggressive, often associated with systemic paraneoplastic symptoms. Molecular predictors of clinical outcome are unknown. While surgery is the mainstay of treatment in the localized setting, management of advanced disease is challenging due to the rarity and poor sensitivity to conventional chemotherapy. This hampers the conduct of large prospective clinical trials. We report herein the first available EHE PDX (developed at the Istituto Nazionale Tumori, Milan, Italy) (INT), to assess the activity of old and new anticancer agents in EHE and inform future clinical studies. We also report on the preliminary results of a prospective observational study, ongoing in collaboration with The EHE Rare Cancer Charity (UK) and The EHE Foundation (US) aimed at identifying prognostic factors. Methods: A PDX of EHE in SCID mice was generated at INT from a patient (pt) suffering with the aggressive clinical variant, presenting with systemic symptoms. The PDX fully reproduces the originating clinical tumor in terms of morphology, biology ( WWTR1- CAMTA1 positive), overall transcriptomic profile. 2D (monolayer) and 3D (sferoids) cell cultures were established following PDX disaggregation. We have started assessing the comparative activity of currently available drugs (doxorubicin, sirolimus), while the efficacy of novel agents is undergoing. In parallel, we analyzed the presence of a selection of inflammatory cytokines in prospectively collected blood samples from EHE pts who entered an international observational study and we looked at their presence and modulation in the PDX model. Results: PDX experiments showed almost negligible activity of doxorubicin while sirolimus induced an 80% tumor volume inhibition. Biochemical analysis in tumors explanted from sirolimus-treated mice confirmed the down-regulation of mTOR downstream signaling. Among all cytokines, Growth and Differentiation Factor-15 (GDF-15) was found significantly over-expressed in serum of EHE pts (n = 23), particularly those with the most aggressive clinical variant, compared to healthy subjects (n = 23; p< 0.01). Consistently, EHE PDX and cell culture were found to release in the blood/culture medium GDF-15. Sirolimus was found to down-regulate GDF-15 release in both in vivo and in vitro EHE models. Conclusions: Our preliminary results indicate that this EHE PDX model is suitable i) for comparatively assessing the activity of anticancer drugs, and ii) for identifying and pre-clinically validating novel biomarkers. The role of GDF-15 in EHE progression deserves further investigation.

Histopathologic diagnosis of endometrial precancers: Updates and future directions.

Early detection of endometrial cancer, especially its precancers, remains a critical and evolving issue in patient management and the quest to decrease mortality due to endometrial cancer. Due to many factors such as specimen fragmentation, the confounding influence of endogenous or exogenous hormones, and variable or overlapping histologic features, identification of bona fide endometrial precancers and their reliable discrimination from benign mimics remains one of the most challenging areas in diagnostic pathology. At the same time, the diagnosis of endometrial precancer, or the presence of suspicious but subdiagnostic features in an endometrial biopsy, can lead to long clinical follow-up with multiple patient visits and serial endometrial sampling, emphasizing the need for accurate diagnosis. Our understanding of endometrial precancers and their diagnosis has improved due to systematic investigations into morphologic criteria, the molecular genetics of endometrial cancer and their precursors, the validation of novel biomarkers and their use in panels, and more recent methods such digital image analysis. Although precancers for both endometrioid and non-endometrioid carcinomas will be reviewed, emphasis will be placed on the former. We review these advances and their relevance to the histopathologic diagnosis of endometrial precancers, and the recently updated 2020 World Health Organization (WHO) Classification of Female Genital Tumors.

Wound fluid sampling methods for proteomic studies: A scoping review.

Understanding why some wounds are hard to heal is important for improving care and developing more effective treatments. The method of sample collection used is an integral step in the research process and thus may affect the results obtained. The primary objective of this study was to summarise and map the methods currently used to sample wound fluid for protein profiling and analysis. Eligible studies were those that used a sampling method to collect wound fluid from any human wound for analysis of proteins. A search for eligible studies was performed using MEDLINE, Embase and CINAHL Plus in May 2020. All references were screened for eligibility by one reviewer, followed by discussion and consensus with a second reviewer. Quantitative data were mapped and visualised using appropriate software and summarised via a narrative summary. After screening, 280 studies were included in this review. The most commonly used group of wound fluid collection methods were vacuum, drainage or use of other external devices, with surgical wounds being the most common sample source. Other frequently used collection methods were extraction from absorbent materials, collection beneath an occlusive dressing and direct collection of wound fluid. This scoping review highlights the variety of methods used for wound fluid collection. Many studies had small sample sizes and short sample collection periods; these weaknesses have hampered the discovery and validation of novel biomarkers. Future research should aim to assess the reproducibility and feasibility of sampling and analytical methods for use in larger longitudinal studies.

Circulating extracellular vesicles expressing PD1 and PD-L1 predict response and mediate resistance to checkpoint inhibitors immunotherapy in metastatic melanoma

BackgroundThe immunotherapy with immune checkpoints inhibitors (ICI) has changed the life expectancy in metastatic melanoma (MM) patients. Nevertheless, several patients do not respond hence, the identification and validation of novel biomarkers of response to ICI is of crucial importance. Circulating extracellular vesicles (EVs) such as PD-L1+ EV mediate resistance to anti-PD1, instead the role of PD1+ EV is not fully understood.MethodsWe isolated the circulating EVs from the plasma of an observational cohort study of 71 metastatic melanoma patients and correlated the amount of PD-L1+ EVs and PD1+ EVs with the response to ICI. The analysis was performed according to the origin of EVs from the tumor and the immune cells. Subsequently, we analysed the data in a validation cohort of 22 MM patients to assess the reliability of identified EV-based biomarkers. Additionally we assessed the involvement of PD1+ EVs in the seizure of nivolumab and in the perturbation of immune cells-mediated killing of melanoma spheroids.ResultsThe level of PD-L1+ EVs released from melanoma and CD8+ T cells and that of PD1+ EVs irrespective of the cellular origin were higher in non-responders. The Kaplan-Meier curves indicated that higher levels of PD1+ EVs were significantly correlated with poorer progression-free survival (PFS) and overall survival (OS). Significant correlations were found for PD-L1+ EVs only when released from melanoma and T cells. The multivariate analysis showed that high level of PD1+ EVs, from T cells and B cells, and high level of PD-L1+ EVs from melanoma cells, are independent biomarkers of response. The reliability of PD-L1+ EVs from melanoma and PD1+ EVs from T cells in predicting PFS was confirmed in the validation cohort through the univariate Cox-hazard regression analysis. Moreover we discovered that the circulating EVs captured nivolumab and reduced the T cells trafficking and tumor spheroids killing.ConclusionOur study identified circulating PD1+ EVs as driver of resistance to anti-PD1, and highlighted that the analysis of single EV population by liquid biopsy is a promising tool to stratify MM patients for immunotherapy.